Category Archives: Science

picoXpert was one of the first (if not THE first) handheld artificial intelligence (AI) tools ever. It provided for the capture of human expert knowledge and later access to that knowledge by general users. It was a simplistic, yet portable implementation of an Expert System Shell. Here is the brief story of how it came to be.

When I was about 10, my grandfather (an accomplished machinist in his day) gave me his slide rule. It was a professional grade, handheld device that quickly performed basic calculations using several etched numeric scales with a central slider. I was immediately captivated by its near-magical power.

In high school, I received an early 4-function pocket calculator as a gift. Such devices were often called ‘electronic slide rules’. It was heavy, slow, and sucked battery power voraciously. I spent many long hours mesmerized by its operation. I scraped my pennies together to try to keep up with ever newer and more capable calculators, finally obtaining an early programmable model in 1977. Handheld machines that ‘think’ were now my obsession.

I read and watched many science fiction stories, and the ones that most fired my imagination were those that involved some sort of
portable computation device.

By 1980, I was building and programming personal computers. These were assembled on an open board, using either soldering or wire wrap to surround an 8-bit microprocessor with support components. I always sought those chips with orthogonality in memory and register architecture. They offered the most promise for the unfettered fields on which contemporary AI languages roamed. I liked the COSMAC 1802 for this reason. It had 5,000 transistors; modern processors have several billion. The biggest, baddest, orthogonal processor was the 16- or 32-bit Motorola 68000, but it was too new and expensive, so I used its little brother, the 6809, which was an 8-bit chip that looked similar to a 68000 to the programmer.

I spent much of the 1980s canoeing in Muskoka and Northern Ontario, with a Tandy Model 100 notebook, a primitive solar charger, and paperback editions of Asimov’s “Foundation” trilogy and sequels on-board (I read them five times).

By the mid 1990s, Jeff Hawkins had created the Palmtm handheld computer. The processor he chose was a tiny, cheap version of the 68000 called the ‘DragonBall’. I don’t know which I found more compelling – this little wonder or the fact that it was designed by a neuroscientist. I finally had in my hand a device with the speed, memory, and portability to fulfill my AI dreams.

The 1990s saw the death of Isaac Asimov (one of my greatest heroes), but also saw me finally gaining enough software skills to implement a few Palm designs. These were mainly created in Forth and Prolog. The Mars Pathfinder lander in 1997 was based on the same 80C85 microprocessor used in the Tandy Model 100 that I had used years earlier. This fact warmed my heart.

However, the time for handheld AI had not yet come. After a couple of years of trying to penetrate the market, I moved on to other endeavours. These included more advanced AI such as Neural Networks and Agent-Based Models. In 2011, I wrote Future Psychohistory to explore Asimov’s greatest idea in the context of modern computation.

Picodoc Corporation still exists, although it has been dormant for many years. It’s encouraging to see the current explosion of interest in AI, especially the burgeoning Canadian AI scene. For those like me, who have been working away in near anonymity for decades, it’s a time of great excitement and hope. Today, I’m mainly into computational citizen science, and advanced technologies, such as blockchain, that might be applied to it.

Personnel motivation and esprit de corps have always been important in any organization. Citizenship and Corporate Social Responsibility (CSR) have sometimes become as important to the brand as the trade name or logo. For many reasons, it is wise to consider institutional citizen science.

Traditionally, participation in citizen science projects has been done at the individual level. That is, observations (e.g. ecosystem projects), identifications (e.g. galaxy classification), and computational contributions (e.g. protein folding simulation) have been made by individuals. People sometimes join teams of like-minded or geographically grouped participants, and their efforts are often reported or tallied as a team. However, there has been very little organizational-level participation. There are many potential benefits of institutional citizen science, and in particular the computational variety.

Employee engagement can be improved. IT staff can provide leadership in setting up the required infrastructure, even with minimal initial effort. They can provide ongoing maintenance, expansion, and IT efficiency improvements. They can learn a lot along the way. Communications staff can prepare and disseminate any required internal information, and again, learn a lot along the way. Seeing the daily progress of the organization’s participation can engage everyone. A well run project can advance the cause of a more inspired, invigorated, enthusiastic, energized, and empowered staff with more of a sense of ownership for their organization. Progress in citizen science projects could be shown in a dedicated section of the organization’s intranet. Perhaps even a big screen could be located in common areas such as the lobby or cafeteria to show live content (e.g. simulations, animations, numerical results) and promote a sense of community. Management can simultaneously learn a lot about concepts such as computing as talent, cognitive computing, ‘gamification’, and integrating technology.

Institutional culture can benefit. Loyalty and pride in the institution are valuable assets. Leadership in ‘doing good’ is a strong motivator and has been a cornerstone of CSR for decades. There are opportunities for recognition and appreciation of both individual and team efforts. Both individuals and groups can suggest which projects to participate in from the large and growing menu available. Citizen science projects offer opportunities for people to think outside the box, to step out of their comfort zone, to consider more diverse possibilities, to form new partnerships, and to take the long view. A culture with all specialists and no generalists needs fresh air to breathe. The study of nature can offer a welcome break from politics and policy considerations, immediately and easily putting everyone on the same level: an observer.

Institutional innovation can benefit. Although tempting (when myopically studying spreadsheets) to farm everything out to consultants and sub-contractors, in-house innovation can be extremely valuable. Skunkworks (small teams for experimental projects) and Bimodal IT (production and exploration as separate yet symbiotic streams) can provide huge benefits, and citizen science is a natural skunkworks project. Notions of siloed knowledge and operation can be skeptically reviewed and perhaps even challenged without having to disrupt the larger organization. ‘What if’ models can move from pure theory to at least partial practicality. Distributed infrastructure is one example, and computational citizen science is all about distributed processing. Owning innovation, moving it vertically through an organization at the appropriate pace, and finally delivering it to the world can generate and cultivate innovation itself as an asset. Like the old proverb says: “Give someone a fish and they’ll eat for a day. Teach someone to fish and they’ll eat for a lifetime.”

Internal HR can benefit. Management and leadership talent can be identified and incubated in a non-threatening, non-competitive domain. Understanding the internal talent ecosystem is essential for the health and future scalability of any organization.

Governments can draw upon citizen science as well. On a regional or national scale, public policy can both encourage and benefit from an actively engaged citizenry. In-depth issues such as climate change, demographic change, disruptive technologies such as Artificial Intelligence (AI) and Automation, and general scientific and digital literacy become much easier to create a dialog around if the communication and participation is two-way. Agile, multi-disciplinary, multi-lingual, and age-spanning efforts are all increasingly valuable. A sparse and diverse population can come together on a unified effort without sacrificing, and perhaps even benefiting from, that diversity.

In the coming age of AI and Computer-Generated Imagery (CGI), there will be a tsunami of hoaxes, spoofing, and fake news. At best, mistaking such things for real content is embarrassing. At worst, these could represent an existential threat. The surest defense against these dangers is scientific literacy, both in the general population, and particularly within the organization. The first step in avoiding a trap is knowing of its existence.

There is also of course, a direct benefit to scientific research. Citizen science is not a replacement for academic research, it’s an adjunct. Projects run under the supervision and purview of scientists benefit in several ways from citizen participation. There is an increase in resources, harnessing more labor (e.g. collecting data), human intelligence (e.g. categorizing images), and computational power (e.g. crunching numbers for simulations). There is an increase in scope, drawing from a wider pool of time, space, and experience. There is also an increase in public awareness of scientific research and methodology. Scientific research is its own reward, and is worth defending.

Finally, there are the usual advantages that come with economy of scale. By gathering the efforts of many individuals under one roof, much wasteful duplication is avoided. Looking at computational citizen science in particular, instead of having members individually setup and run their own ‘crunching’ computers at home, they can participate at the workplace or remotely (perhaps using the ‘cloud’). The performance per watt of one big machine is much better than many smaller ones. It’s also a way for social skills to be advanced over isolation in the internet age.

Organizational learning requires interaction and participation. Growth requires innovation. Basic scientific literacy improves objectivity and comprehension of a complex world. Computational thinking improves problem solving skills. Improved use of reason and logic for analytical thinking, deduction, and inference might result. These skills and attitudes may not be easily quantified or measured, yet they surely benefit the organization, especially in the long term. Learning becomes an organizational task and goal, resulting in a more knowledgeable enterprise as a whole. Improvements in individual skills, together with deeper and wider internal communication go a long way toward that end. Diversity of learning styles, participation levels, and paces can be accommodated. The best organizations assign the ends, not the means.

“If you want to build a ship, don’t drum up people to collect wood and don’t assign them tasks and work, but rather teach them to long for the endless immensity of the sea.”– Antoine de Saint-Exupéry

Centuries past are like the long night, a mysterious dream world. The modern, networked Earth is like daybreak, a world-wide awakening.

The fifty years 1945-1995 could be considered as a sort of ‘twilight zone’ between the two. The bulk of thinking and research done during this period is too new to be of any great historical or even deep nostalgic interest. Yet it is too old to be treated as current knowledge in the Internet age, where freshness and novelty are valued. In addition, we are still inside several of the epic transformations that began in this time. It’s difficult to appreciate and comprehend a revolution when you live within it. Our blind spot for this period is a great tragedy.

This period could be called the ‘Asimovian Epoch’. Here’s why.

Isaac Asimov (1920-1992) was perhaps the last great polymath. He was a biochemistry professor and author, publishing (writing or editing) over 500 books (covering 90% of the Dewey Decimal Classification system) and many thousands of shorter works. That’s an average rate of over one book per month over his long career – one of the greatest outbursts of creativity in history. His favorite topics included history, physics, comedy, and English literature. He was quite arguably the greatest science fiction writer of all time. He, Robert Heinlein, and Arthur C. Clarke are known as ‘The Big Three’ of the Golden Age of science fiction.

Asimov was a nexus of 20th century thought.

He was strongly influenced by Greek and Roman classics, Shakespeare, Gilbert & Sullivan, and also by contemporaries such as Kurt Vonnegut, Heinlein, Clarke, Harlan Ellison, Carl Sagan, and Marvin Minsky. He in turn influenced the likes of Paul Krugman (Nobel Prize in Economics), Elon Musk (SpaceX, Tesla), most science popularizers (happily now becoming too numerous to list), virtually all science fiction writers, and of course millions of readers.

Optimism came early to Asimov, perhaps because he spent so much time as a child in his father’s candy store. Many were initially drawn to science as teenagers by his optimistic and compelling vision of science and technology. Given the advances that 20th century science brought, such as space exploration, computation, automation, microbiology, etc, our debt to him on this account alone is inestimable. His masterpiece, Foundation, has deeply influenced many others, including me: Future Psychohistory

It has become fashionable for some to be suspicious of science and even liberal education. This was a trend that Asimov fought tirelessly against all his life. He warned againstthe false notion that democracy means that ‘my ignorance is just as good as your knowledge’

He went even further, exploring the frontiers of individuality and self-education:Self-education is, I firmly believe, the only kind of education there is.

Some even credit Asimov with predicting the Internet, especially its use for personalized education. He envisioned a world in which people could pursue their own interests, unfettered by the standard classroom, open to new ideas and serendipity.

Ironically, the very technology that he hoped would spur individuality and diversity has the opposite effect at times. One example is digitization and archiving of published material. There was a time when books were expensive, highly personal possessions that stayed with the owner for a lifetime, and were even sometimes handed down across generations. People would store letters, pressed flowers, and other snippets of life between their pages, and write personal margin notes. Books were not just repositories of language, they were time capsules and valuable historical accounts. However, the best book to scan is a pristine, un-personalized volume. Once it is scanned, future researchers use this one version increasingly exclusively. Individuality and diversity are squelched.

A pervasive basic understanding of science was Asimov’s constant goal. He often took the opportunity to describe science not as a storehouse of absolute truth, but rather as a mechanism for exploring nature. Science is not a dogmatic belief system. Learning happens when one asks questions of the universe and then listens attentively and objectively with the ears of science. He wrote eloquently on scientific models, logic, and inference. An example is this essay.

Of course Asimov is best known for his robot stories, and his ‘Three Laws of Robotics’. In 2010, the U.S. Congress established National Robotics Week in his honor. It’s possible that he saw artificial intelligence as a way for intellect to exceed the bounds of human bias, subjectivity, and mortality.

I never met Isaac Asimov, but I have spoken to several people who did have that pleasure. Each one related the profound impression he made on them. He died in 1992, but his books continued to be published posthumously. By the end of 1994, the World Wide Web Consortium had been founded at MIT. The dramatic rise of the Web marks the end of the Asimovian epoch.

Academia can sometimes degenerate into a vicious paper chase, with Ego in the front seat,
Science in the back seat, and Humanity locked in the trunk. Asimov famously said,

The most exciting phrase to hear in science, the one that heralds new discoveries,
is not ‘Eureka!’ but ‘That’s funny…’

Sadly, with hyper-specialization and the loss of interdisciplinary thinkers like Isaac Asimov, the catch-phrase of science has increasingly become: “Oooh! Shiny!”

There are several ways to categorize programming languages. One is to distinguish between applicative and concatenative evaluation. Most languages are applicative – functions are applied to data. In contrast, a concatenative language moves a single store of data or knowledge along a ‘pipeline’ with a sequence of functions each operating on the store in turn. The output of one function is the input of the next function, in a ‘threaded’ fashion. This sequence of functions is the program. Forth is an example of a concatenative language, with the stack serving as the data store that is passed along the thread of functions (‘words’). “Forth is a simple, natural computer language” – Charles Moore, inventor of Forth.

One of the great advantages of concatenative languages is the opportunity for extreme simplicity. Since each function really only needs to know about its own input, machinery, and output, there is a greatly reduced need for overall architecture. The big picture, or state, of the entire program is neither guiding nor informing each step. As long as a function can read, compute, and write, it can be an entity unto itself, with little compliance or doctrine to worry about. In fact, in Forth, beyond the stack and the threaded execution model, there’s precious little doctrine anyway! Program structure is a simple sequence, with new words appended to the list (concatenated). The task of the programmer is just to get each word right, then move on to the next.

In nature, the concatenative approach is the only game in town. Small genetic changes occur due to several causes, random mutation being one of them. Each change is then put through the survivability sieve of natural selection, with large changes accumulating over large time scales (evolution). (Evolution is active across the entire spectrum of abstraction levels. Hierarchies emerge naturally, not through doctrine or design.) Concatenation is the way by which these small changes are accumulated. Much of the epic and winding road of human evolution is recorded in our DNA, which is billions of letters long.

This process can be seen happening right now in molecular biology. Consider the ribosome. This is the little machine inside a cell that reads a piece of RNA (a chain of nucleotides) and translates it into a protein (a chain of amino acids). There is no Master Control Program assigning names, delegating work, and applying functions. There is only a concatenative program, built up over the ages by evolution. So, basic life uses a fairly powerful and successful form of computation: DNA for storage, RNA for code, ribosome for computing, protein for application.
(and natural selection for testing) 🙂

We flatter ourselves when we talk of our ‘invention’ of levers, gears, factories, and computers. Nature had all that stuff and much more long before we ever came down from the trees. Math, engineering, and science are great not because of their products, but rather because they enable 3-pound hominid brains to explore nature and ponder the possibilities.

I get nervous when people enthusiastically advocate models. This happens mostly, though not exclusively, in sociopolitical debates such as climate change. A scientific model is not a belief system. All those who claim that science is on their side might better consider whether they are on science’s side. Most scientific models, especially statistical ones, are ways to measure and predict the behaviour of a bounded system (a subset of nature). Such a model does not claim to be the final, fundamental, and ultimate description of nature itself. It’s important to not confuse the map with the territory, since rabbit holes rarely appear on maps.

In fact, even a major shift in the underlying paradigm may only correspond to a small change in a model’s accuracy. A very good essay on this was penned by Isaac Asimov. In short, he argued that models may be updated with more subtlety and scope, but they are not summarily discarded as simply and absolutely wrong. Long after we replaced force with space-time geometry in our gravitational models, we still design elevators with Isaac Newton looking over our shoulder. We are all very much still ‘Flat Earthers’ when we plan a walk (unless that walk will take us across a time zone boundary or we sneak a peek at our GPS location along the way).

In this view, science is models and observations only. A theory is only a specific model, not a deep insight into the true nature of reality. Nature simply is. That’s it. Our beliefs about its fundamental structure are illusions. The meaning of observations is model dependent, but the observations themselves are not. Antares is in the same position in the sky, whether it is a red supergiant or “the heart of the scorpion”.

It should be noted that not everyone agrees with this view. For example, Thomas Kuhn argued that newer models are not just ‘better approximations’ (straw man alert) and that it is a serious mistake to teach that they are. He argued that consensus of the scientific community is also a component of scientific truth. I don’t happen to share that view, but then as I consider consensus to be irrelevant in science, my opinion means naught anyway. Of course paradigm shifts and revolutions do happen, but they are artifacts of history and psychology, not objective science. It’s a fallacy to define science as being only the set of all scientists, past and present. Again, there’s that map and territory confusion.

Raw predictive power alone is not the only metric for judging a model. Probably the most universal heuristic is agreement with observation. Many a compelling model has been ruined by the facts. Mathematical beauty, symmetry, and ‘feel’ are not important. Elegance and simplicity are helpful, perhaps even necessary, but not sufficient conditions for a good model. Enthusiasm for a model is a strong cause for suspicion. Humility before nature is a much better heuristic. Darwin and Planck had to be dragged along reluctantly with their own theories. Models can incorporate ‘tricks’ that are obviously not part of reality. For example, adding an equal ‘negative mass’ allows one to easily calculate the centre of mass of a plate with a hole in it. Perhaps the best thing a model can do is to not just answer questions, but to ask even better ones. Heliocentricity and the microbe theory of disease launched tremendously fruitful new science.

Nature is a realm of computation and evolution on an unimaginable scale (quite literally). Local agency leads to emergent complexity. Mathematics is one of the tools that can allow a vastly simplified model of nature to be stored in a three-pound hominid brain. However, daisies and snails know nothing about mathematics. Anthropomorphization of nature is not science. Consensus and comfort are not science. Clinging to teddy bears is really cute in youngsters, not so much in adults. The good news is that nature is far, far, far more wonderful than anything any individual or advocacy group can come up with (including well-crafted teddy bears). You just have to let objectivity take the wheel.

In ancient times, love of wisdom had a name – “philosophy”. That context brings to mind sun-drenched hilltops in Greece and great libraries like Pergamum and Alexandria. Scholars were few, ‘pure’, supported by patrons or states, and breathed the rarefied air of privilege.

In modern times, we talk of “science”. There are many, many scientists around the world. They work on increasingly specialized research, and increasingly on applied science. They are largely supported by a vast international academic system that is quickly merging with complex economic systems. In fact, direct support from the commercial and corporate world is becoming the norm. Many scientists now breathe the chemical soup of industrialization.

The general public has the widest technology usage rate in history. There are billions as many transistors as people and the number of cell phone accounts is roughly equal to the world’s population.

Technology is almost becoming synonymous with science. “Techno-Science” has reached a level of acceptance and respect that was previously reserved only for religion. Even many of those who remain scientifically illiterate hold science in mystical reverence.

This is the greatest tragedy of all.

The death of pure science directly implies the death of human civilization. We will either be enslaved in a dystopian future or simply be replaced by artificial intelligence. It ends not with a bang, but a whimper.

In 1969, Robert R. Wilson, an advocate of a new particle accelerator research facility that later became Fermilab was testifying before a committee in Washington, DC. His exchange with one Senator has become legendary and is found in many places on the Internet, with Wilson himself memorialized at Fermilab.

Senator: “Is there anything connected with the hopes of this accelerator that in any way involves the security of the country?”
Wilson: “No sir, I don’t believe so.”
Senator: “Nothing at all?”
Wilson: “Nothing at all.”
Senator: “It has no value in that respect?”
Wilson: “It has only to do with the respect with which we regard one another, the dignity of men, our love of culture. It has to do with those things. It has to do with, are we good painters, good sculptors, great poets? I mean all the things that we really venerate and honor in our country and are patriotic about. It has nothing to do directly with defending our country except to help make it worth defending.”

There’s little I can add to that. Thomas Huxley (“Darwin’s Bulldog”) once said,